Colour fusion effect on deep learning classification of uveal melanoma.

Background: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance.

Methods: A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal naevus.

Automated segmentation for early detection of uveal melanoma.

Objective: Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This study explores the potential of machine learning to automate tumour segmentation.

Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning.

Objective: This study aims to assess a machine learning (ML) algorithm using multimodal imaging to accurately identify risk factors for uveal melanoma (UM) and aid in the diagnosis of melanocytic choroidal tumors.

Subjects And Methods: This study included 223 eyes from 221 patients with melanocytic choroidal lesions seen at the eye clinic of the University of Illinois at Chicago between 01/2010 and 07/2022. An ML algorithm was developed and trained on ultra-widefield fundus imaging and B-scan ultrasonography to detect risk factors of malignant transformation of choroidal lesions into UM.

Color Fusion Effect on Deep Learning Classification of Uveal Melanoma.

Background: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of color fusion options on the classification performance.

Methods: A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal nevus.

Prevalence, Awareness, Treatment, and Control of Hypertension in Young Adults (20-39 Years) in Kerala, South India.

Objective: We sought to study the prevalence of hypertension and the levels of awareness, treatment and control of hypertension in the young adults in Kerala, India compared to older adults.

Methods: We identified 1,221 young adults (men 36.7%) in the age group 20-39 years from the 5,150 participants of the Cardiological Society of India Kerala Coronary artery disease (CAD) and its Risk factors Prevalence (CSI Kerala CRP) Study.